Method for matching queries with answer items in a knowledge base

ABSTRACT

The present invention includes an expert system in which a search index furnishes answers to incoming queries provided in natural language. A search index for a specific field contains components that facilitate selecting a best fitting stored answer to the incoming query. Furthermore, context of the incoming query (e.g. location of the user, a current web page or service being used/viewed by the user, the time, etc.) may be considered when selecting a best fitting answer.

PRIORITY CLAIMS

This application is a continuation of: U.S. patent application Ser. No. 14/311,441, filed on Jun. 23, 2014 by the inventors of the present application and titled: “Method for Matching Queries with Answer Items in a Knowledge Base”;

-   U.S. application Ser. No. 14/311,441 is a continuation in part of:     U.S. patent application Ser. No. 13/757,940, filed on Feb. 4, 2013     by the inventors of the present application and titled: “Method for     Matching Queries with Answer Items in a Knowledge Base” and issued     as U.S. Pat. No. 9,110,978 on Aug. 18, 2015; -   U.S. patent application Ser. No. 13/757,940, is a continuation     application of U.S. patent application Ser. No. 13/019,318, filed on     Feb. 2, 2011 by the inventors of the present application and titled:     “Method for Matching Queries with Answer Items in a Knowledge Base”     and issued as U.S. Pat. No. 8,407,208 on Mar. 26, 2013;     each of the aforementioned applications is hereby incorporated     herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to an expert system, functional for providing answers to users presenting queries in specified fields.

BACKGROUND OF THE INVENTION

Expert systems attempt to provide answers to problems in a specific domain, with the intent of at least partially replacing human experts. Expert system reasoning often tries to imitate human reasoning. Since expert systems are to do with human reasoning and communications, understanding and utilizing human words is a crucial factor if a natural language is used for querying the expert system. The two most significant components of an expert system are the expert system shell and the knowledge base. The expert system shell handles interpretation of user input into the system in order to facilitate the reasoning. The knowledge base of an expert system is the factual body consisting of information and relationships between the elements of information arranged in such a way as to fulfill the purpose of the expert system. The knowledge base does not necessarily contain definitive rules to prioritize between options or rule out others altogether.

The Internet offers a practical medium for users to interconnect with an expert system. The convenience and availability of Internet browsers enables most users to benefit from the knowledge managed by specific expert systems almost without limitations of location. Moreover, the ubiquitous access users may now have to knowledge bases through personal hand held communications end points, encourages the need for the diversification in knowledge domains, so that one could derive quick answers for an increasing amount of fields of knowledge.

SUMMARY OF THE INVENTION

The present invention includes an expert system in which a search index furnishes answers to incoming queries provided in natural language. A search index for a specific field contains components that facilitate selecting a best fitting stored answer to the incoming query. Furthermore, context of the incoming query (e.g. location of the user, a current web page or service being used/viewed by the user, the time, etc.) may be considered when selecting a best fitting answer. A language specific storehouse of weighted words and a private storehouse of weighted words associated with a field-specific search index provide the basis for evaluating the significance level of a natural language word of a query. Again, context of the incoming query may be considered when evaluating the significance level of a natural language word of a query. Irrelevant portions of an incoming query may first be deleted from the inquiry prior to processing. A procedure elects candidates from a store of indexed answers to match the incoming query to first form a list of candidates, based on the existence of identical or similar words. Then, from the list of available candidates, one that provides the best match is selected. The final selection takes into account the influence of the excess of less significant words. A candidate is derogated if it is found to contain a larger number of less significant words as compared to another candidate containing a smaller number of insignificant words.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may best be understood by reference to the following detailed description of non-limiting exemplary embodiments thereof when read with the accompanying drawings in which:

FIG. 1A is a graphical presentation of an exemplary flow of words arriving from a user to be subsequently given a significance level;

FIG. 1B is a graphical presentation of an exemplary summary of the contesting routine;

FIG. 2A is a graphical presentation of an exemplary usage of a plurality of search indexes;

FIG. 2B is a graphical presentation of exemplary components of the indexing component;

FIGS. 3A-3C are a series of graphical presentations of an exemplary sequence of events carried out to produce similar word scores of the comparison types;

FIG. 4 is a graphical presentation of a situation in which a number of reference lists are associated with one indexed answer item.

FIG. 5A is a flowchart including exemplary steps of processing a natural language incoming query to select one or more matching indexed answers, including the removal of irrelevant words from the query, all in accordance with some embodiments of the present invention.

FIG. 5B is a flowchart including exemplary steps of processing a natural language incoming query to select one or more matching indexed answers, all in accordance with some embodiments of the present invention.

FIG. 6A is a flowchart including exemplary steps of determining significance values of words of a query, including the removal of irrelevant words from the query, all in accordance with some embodiments of the present invention.

FIG. 6B is a flowchart including exemplary steps of determining significance values of words of a query, including the removal of irrelevant words from the query and factoring of synonyms of each word in the query, all in accordance with some embodiments of the present invention.

FIG. 6C is a flowchart including exemplary steps of determining significance values of words of a query, in accordance with some embodiments of the present invention.

FIG. 6D is a flowchart including exemplary steps of determining significance values of words of a query, including factoring of synonyms of each word in the query, all in accordance with some embodiments of the present invention.

FIG. 7A is a flowchart including exemplary steps of compiling lists of candidate matches of an IQ, in accordance with some embodiments of the present invention.

FIG. 7B is a flowchart including exemplary steps of compiling lists of candidate matches of an IQ, including factoring of synonyms of each word in the query, all in accordance with some embodiments of the present invention.

It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements.

It should be understood that the accompanying drawings are presented solely to elucidate the following detailed description, are therefore, exemplary in nature and do not include all the possible permutations of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present invention.

Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as “processing”, “computing”, “calculating”, “determining”, or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities within the computing system's registers and/or memories into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices. The term server may refer to a single server or to a functionally associated cluster of servers.

Embodiments of the present invention may include apparatuses for performing the operations herein. This apparatus may be specially constructed for the desired purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, memory cards (for example SD card), SIM cards, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs) electrically programmable read-only memories (EPROMs), electrically erasable and programmable read only memories (EEPROMs), magnetic or optical cards, or any other type of media suitable for storing electronic instructions, and capable of being coupled to a computer system bus.

The processes and displays presented herein are not inherently related to any particular computer, communication device or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the desired method. The desired structure for a variety of these systems will appear from the description below. In addition, embodiments of the present invention are not described with reference to any particular programming language or markup language. It will be appreciated that a variety of programming languages or techniques may be used to implement the teachings of the inventions as described herein.

The present invention is a multi-field expert system, meaning that implementing the invention will facilitate for a user the extraction of data in a plurality of fields of knowledge. For each of those fields of knowledge, a knowledge base including a unique index is created that contains the knowledge of that specific field, and provides a means to retrieve that knowledge in response to natural language queries. Furthermore, each unique index may include indications of context of each content contained in the knowledge base to be considered when selecting a best fitting answer to a given query.

In accordance with the present invention, queries are presented as electronic signals representing natural language words to a search index (SI). The queries are further analyzed and responses provided, based on the existing knowledge base for that specific SI and, optionally, also based on the context of the incoming query. It is also possible to direct searches to multiple fields at a time. An additional feature of the present invention is that each incoming query (IQ) having been reciprocated by an answer can and may be used to contribute to the performance of the knowledge base. This aspect of the invention will be explained later on. The analytic approach, in accordance with the present invention, attempts to analyze an incoming query and find an indexed answer item (IAI) that the expert system would be able to further use in an optimal way to provide the best answers.

An IQ undergoes a process which, for reasons of convenience of explanation, will be considered hereinafter as consisting of several procedures, some of which are executed in an ordered succession. The first procedure is a pre-processing procedure (PPP), in which an incoming query is evaluated for some administrative properties, for example language, general field of commerce, technology, context etc, and for misspelling and validity. Additionally, in the PPP, a private storehouse of weighted words is assigned to the IQ. Further, irrelevant portions of the query may be removed prior to further processing, i.e. words irrelevant to the substance of the query (for example, superfluous adjectives, articles (e.g. “a”, “the”, etc.) and other parts of speech that do not add specific meaning to a sentence). According to further embodiments, irrelevant portions of a query may also be determined based on context (for example, the terms “know what I mean” may be irrelevant when appearing together, whereas each term may have relevance when appearing separately). Some of the consequences of the PPP will be dealt with below.

Subsequently, in the analytic procedure performed on the IQ, a significance level score is to be given to the fragments of the IQ, this will be explained with reference to FIG. 1A, showing a symbolic description of the way in which it is accomplished. Incoming query 20 arrives, for example, by way of a communications network running a TCP/IP communications protocol and is received by a server connected to the network. Such a server invokes a program, extracting information from the natural language (NL) query arrived, which includes the words and possibly additional information as to which of the one or more SIs are to be searched. Assuming, by way of example, that the query is a sentence composed of a succession of words making sense to a person versed in a specific language and having practical knowledge in a field about which the knowledge base is chartered to provide a service, the IQ is first fragmented and in a typical case, information extracted. The fragments form a succession of natural language words (NLWs) lined up in a queue 22 to be submitted each to a contesting routine 24, in which each word is weighed by finding a match in a natural language word storehouse 26 of weighted words. In the storehouse of words, each entry, such as a word symbolized by NLW2, has an associated numerical weight. Typically, the NLWs are processed in the order in which they arrive. The role of contesting routine 24 is to determine the significance level (SL) score associated with each incoming word, designated NLWT1, NLWT2 etc. The temporal succession of words of original IQ 20 is then reordered to give a new succession 28 referred to hereinafter generally as ordered query word succession (OQWS), in which the NLW with the highest significance level becomes associated with the highest position in the new succession, as described graphically, a certain NLW of the IQ becomes associated with SL score A, and a certain, other NLW becomes associated with a SL score “least”. Other NLWs occupy intermediate positions in the OQWS as determined by their level of significance (SL). It cannot be ruled out, statistically, that on occasions the new order is the same as the order of arrival of NLWs. The storehouses of weighted words are dynamic entities which are constantly recompiled and their maintenance will be discussed later on. The assignment of a significance level to each of the incoming NLWs marks the completion of the contesting routine 24 as relates to query 20. As an example, in the question: “where can I find a library?” the word “I” is almost insignificant, the same can be said about “can”. The word “find” is however more indicative because it relates to a fact that the person asking is searching for something which has practical meaning. The most significant word would be in this case “library”. However, in other examples, the same word may receive different weights. Context of the incoming query may also be considered when assigning SL scores (For example, in a query relating to geographical/locational question, words representing physical locations may be assigned higher SL scores). Contesting routine 24 which determines the SL score of each NLW (in this case from the IQ), operates as follows:

Routine 24 references both NL storehouse of weighted words 26 which is private in the context of the current SI, and storehouse 30 which is common to all SIs for the same language (i.e. English). Each NLW storehouse (the SI private storehouse 26, and the language storehouse 30) maintains a respective SL score for each contained NLW. These scores are determined by distributing the NLWs by their respective usage frequency on a logarithmic scale, such that the most common word has the lowest SL score, and the least common NLW gets the highest score. According to some embodiments, each NLW storehouse (the SI private storehouse 26, and the language storehouse 30) may maintain multiple SL scores for each contained NLW, dependent on the context of the query. For example, the word party may be uncommonly used between the hours of 8-5 and very frequent between the hours of 5-10 pm, or the word team may be scarcely used by viewers of fashion related webpages and commonly used by viewers of sports websites. In other words, the SL scoring process may be dynamic and dependent on the context of the incoming query. Typically resulting scores are normalized to a scale of 0 to 100. In the routine, each NLWT obtains a SL score by weighing both the SL score component of the private storehouse 26 and the SL score component obtained from the language storehouse 30, such that the significance given to each word in an IQ is a value reflecting its combined relevance both with respect to the present SI and field of knowledge and with respect to the language generally. To demonstrate the difference in the two aspects, the word ‘shoe’ can be considered indicative of context in the scope of the English language, but is very common and much less significant in the scope of a footwear related knowledge-base.

According to further embodiments, synonyms may be considered when determining SL scores. For this purpose, each NLW may be automatically correlated to multiple NLWs having the same meaning prior to assigning a SL score to the NLW. For example, the words blouse and shirt may be considered equivalent when assigning SL scores to either word. In this manner the word blouse may receive a relatively low SL score even though it is uncommonly used, due to the fact that the word shirt is very commonly used. According to yet further embodiments, a degree of synonymity/similarity between the synonyms may be factored when determining the SL score for one of the synonyms and/or a commonality of the use of a certain synonym in place of its synonymous pair may be factored into the degree of synonymity. Further, in the later process of comparing IQs to indexed answer items matches of NLWs to synonymous NLWs may incur a match penalty which may be relative to the degree of similarity between the significances of the pair of synonyms. Accordingly, each recorded synonymous word pair may further include a value representing the similarity between the words of the pair. This value may be factored when determining an SL score for one of the words of the pair as well as during matching of NLWs in an IQ to indexed answer items. According to further embodiments, a value representing the similarity between the words of a pair of synonyms may also be context dependent, as some pairs of synonyms are more or less similar in significance in different contexts.

Finally, after each word is assigned its SL score, the words are reordered by their SL score and the SL scores are typically normalized. In a preferred embodiment of the invention, the scores are normalized, starting with the most significant NLW, which is typically set to a 100 (or 1, depending on the scale). Thus the second NLW is assigned, for example, to 75. The ratio between the references is typically kept the same after normalization. For example if NLWA was 50 before normalization, after normalization it becomes 100, and the next NLW, SLWB was 25, is normalized to 50, keeping the same ratio with the first NLW normalization factor. To summarize the issue of associating each word of queue 22 with a SL score, reference is made to FIG. 1B. NL word NLWT1 is contested with private storehouse of words 26, receiving a SL score component 32. Concomitantly, language storehouse of words 30 is contested with NLWT1, producing a respective SL score component 34. Both SL score components are then combined to produce a SL score which is now associated with NLWT1. The NLWs of a certain IQ are then arranged in a OQWS 28, in the order of their respective SL scores. One example of attaining such a combination is by performing weighted averaging, while giving one factor a different weight than the other. Finally their SL score is normalized, in the OQWS.

The Search Index (SI) Component, and the Searching and Finding Best Match Procedure (SFBMP): Traversing IAIs

The next procedure, which follows in the analytical sequence, is the selecting of the best matching indexed answer item for the IQ out of a selected few. Before explaining the procedure, the index component of a system of the invention is introduced. First, reference is made to FIG. 2A. For each private storehouse of weighted words a certain SI 36 exists which relates to this private storehouse. Thus SI 36A relates to private storehouse of words 26A of a certain field of knowledge, and SI 36B relates to private storehouse of words 26B of another field of knowledge. Again, according to some embodiments, each SI may be dynamic and dependent on the context of an incoming query. Thus, SI 36A may be slightly different in morning hours as opposed to evening hours or dependent on the type of webpage the user was viewing when entering the query or dependent on the current location of the user, etc.

As explained above, private storehouse of weighted words 26 contains a list of words each associated with a weight which reflects its potential significance (possibly dependent on the context of the query). More is described about the structure of SI with reference to FIG. 2B. SI 36 includes a list of indexed keywords 38, all of which keywords bear equivalence to a respective word in a private word storehouse 26, thus IKW1, an indexed key-word, is equivalent to NLW1 in the private storehouse 26 and also equivalent to a NLW in the language storehouse 30, for example NLWa. Each indexed keyword is associated with a list of Indexed Keyword References. For example list of reference (LOR) 40 is associated with indexed word IKW1, and list of references (LOR) 42 associated with indexed word IKW2. An indexed keyword reference (IKW REF) represents an association of an IKW and an IAI with a SL score, typically calculated by contesting routine 24. A LOR may sometimes contain only one reference. In each LOR, the order of references is the order which quantitatively reflects the SL score of the association between the IKW and the IAI, presented as a numeric value, meaning the IKW REF that bears the strongest association with an IAI will be positioned first in the LOR. For example, LOR 42, contains several references, all associated with indexed key word IKW2, and the first reference, designated 46 bears the highest SL score of all IKW References in LOR 42. The term IKW reference, stems from the fact that each IKW reference (IKW REF) in index 36 refers to only one specific indexed answer item (IAI) (one-to-one relationship). The mutual relationship is not symmetrical, and each IAI can refer or receive reference to and from a number of IKWs (one to many relationship), the only restriction is that an IAI can refer to only one IKW REF in a specific LOR. Each IAI points to an Internal List of References (ILOR), containing all the references pointing to it, possibly from a plurality of LORs. For example IAI2 designated item 68, contains ILOR 78 that refers to two IKWs, i.e. IKWN REF1 designated item 70 of LOR 44 and to IKW2 REF2 designated item 72 of LOR 42.

Searching and Finding a Best Fitting Indexed Answer Item for an IQ (SFBMP)—Making a Collection of IAIs, a List of Candidates

Once a new query is presented to the system, contesting routine 24 constructs a OQWS, containing a weighted list of natural words. Then, the SFBMP is invoked. For each NLW of the IQ, a search is conducted finding the appropriate IKW in the appropriate SI. NLWA is the first word in the OQWS, having a highest SL score. It is associated with NLW1 in the private storehouse of weighted words 26 (and NLWa of the language storehouse 30), and with an indexed keyword IKW1. The SFBMP searches, proceeding to traverse each of the LORs referenced by the IKWs referenced by the search query, and for each of the IKW REFs in those LORs, it will in turn traverse the ILOR contained in each IAI referenced by a traversed IKW REF.

There are two traversal strategies available for determining the order of traversal of each LOR, the sequential strategy, in which traversing the REFs is in order of SL scores (LORs are sorted by SL score), and another strategy, a “Similarity First” search strategy, the traversal of the LOR starts from the IKW REF having a SL score most similar to the weight in the NLW for the IQ, and subsequently continues to the least similar one (possibly favoring higher positive or negative differences between the SL similarity making the search expansion asymmetrical). In addition, in a preferred embodiment of the invention, both strategies may only traverse a portion of the LOR, the portion size may be in proportion to the SL for the contested NLW in the IQ, bringing about an effective mechanism to limit traversal for low SL score LORs, which are by definition longer and generally contain less relevant IAI. For example: For NLWA with SL=100, 100% of the LOR's references will be traversed, and for NLWB with SL=50, only 50% of the LOR's references will be traversed.

To demonstrate a sequential search procedure, the following example is given: Looking first in LOR 40 associated with IKW1, to find its highest SL scoring IKW1 REF, i.e. IKW1 REF 1. This demonstrates the fact that the order of NLWs in the OQWS dictates the order of search inside the SI. The higher the SL scoring of a NLW in the OQWS is, the earlier it will be visited. In another example (also of a sequential strategy): NLWn is a NLW in the private storehouse of weighted words 26, equivalent to IKWN in the index. Associated with IKWN is LOR 44, and the highest SL scoring IKWN reference in LOR 44, IKWN REF1 is automatically selected as a first reference to be traversed in the search. This IKWN REF1 designated 70, points at IAI2, designated 68, at this point the procedure will initiate traversal of IAI2's ILOR 78 until completing all items referenced in the IAI's ILOR (in this example the next item to be traversed will be IKW2 REF2 designated 72), and then continue the traversal of the IKWN's LOR (in this example LOR 44) and the next item to be traversed will be IKWN REF2. When finishing one NLW, the procedure continues to the next NLW, i.e. NLWB in OQWS 28, etc.

During the search procedure, if an IKW points at an IAI which was already traversed in the current SFBMP (typically by a reference from a different LOR) it is not processed again. As the procedure traverses index 36, for each traversed IAI, a match level score (MLS) is calculated that is indicative of the perceived similarity between each traversed IAI and the IQ. During the traversal, a collection of relevant IAIs is accumulated, referred to hereinafter elected IAIs. In order to obtain from a list of elected IAIs the best matching IAI for the IQ, the calculated match level score MLS will ultimately be used to determine the best match, in response to the query.

SFBMP: Calculating the Match Level Score (MLS)

MLS calculation is accomplished in two steps. The first step calculates a Similar Words Score (SWS) by matching two sets of NLWs having each a respective SL score per contained NLW, for example a set of NLWs in the OQWS and an indexed set in the elected IAI. To calculate the SWS, for each NLW in the OQWS, a matching IKW REF is searched for in the tested IAI's ILOR, and if a match is found, a score is calculated. The meaning of a match in this case is either an identical word, or an equivalent word or expression (such as a synonym). The score is accomplished by combining the SL score of the IKW REF with the SL score of the NLW in both the OQWS and the respectively equivalent IKW REF, for example by multiplying the respective SL scores, and optionally applying a penalty or bonus for a high difference or high similarity between the two SL scores, respectively. The combined scores from all NLWs and equivalent IKWs, respectively, are accumulated to create the SWS. An example is given below.

In addition to the MLS calculation accomplished by comparing the elected IAI and the OQWS and obtaining the pertinent SWS score, another type of SWS score is obtained. This other type is obtained by computing the self matching for each elected IAI, or in other words using own components for each respective elected IAI only and in a similar way by self matching regarding the OQWS. These two SWS sub-types are referred to hereinafter in general as Perfect-Match-Scores (PMSs). As can be seen in FIG. 3A, SWSs 94, 96 and 98 are generated in a procedure that exploits the difference between a OQWS 100 of an IQ, and the elected IAIs obtained during the run of the SFBMP procedure, using both the OQWS and the respective IAIs, IAI 102, IAI 104 and IAI 106. The two sub-types of PMSs typically generate the highest possible SWS for each respective NLW set, accordingly. PMS 110 results from self comparing of the OQWS words and PMS 112, 114 and 116 results from the self comparing of IAI 102 to IAI 106, respectively. In FIG. 3B, the computation of SWSs calculated for the comparison of an elected IAI with the OQWS, and referred to hereinafter as CSWS, is pictorially explained. OQWS 212 contains a list of NLWs arranged in the order of the SL scores. Several IAIs found as described above due to their containment of indexed natural language words (INLWs), IAI1, IAI2 and IAI3, all contribute, in an order of their SL scores, combining to form SWS. Thus, the respective SL scores of NLW 214 of OQWS 212 are combined with INLW 214A in accordance with a calculation rule 218. A combination computation score (CCS) 220 is obtained. Then, NLW 224 is found no match in IAI1, but NLW 226 combined as before (same rule 218) with INLW 226A of IAI1, to yield CCS 230, the order of computation dictated by a traversal strategy discussed above. The CCSs are summed up to produce a CSWS, and the number of CCSs produced or used for calculating the CSWS is predetermined or dynamically determined or not limited at all. After the traversal of IAI1 is completed, or in parallel, as in FIG. 3C the INLWs of IAI2 are traversed and combined with equivalents, if available, in an order dictated by the according to traversal strategy. CSWS2 is produced by summing up the CCSs.

Finally, in the second step, the MLS calculation includes the combining of the difference between (a) the SWS, and (b) each of the two PMSs generated as described above. The combining is not necessarily balanced, meaning, for example, that the difference between the CSWS and the PMS may receive a higher factor in the calculation of the final MLS and vice versa.

For example:

-   1. PMS for a OQWS with 2 NLWs having SL scores 100 and 50     respectively is calculated as follows (such a typical calculation     multiplying each score by itself as if a match was found):     (100*100+50*50)=12,500 -   2. PMS for IAI with 2 NLWs with SL scores 100 and 70 respectively     is:     (100*100+70*70)=14,900 -   3. CSWS (assuming that only the 1 NLW appears in both items, and     that NLW is the one that scored 100 in both sets of keywords, each     of the SWS), is calculated as follows:     (100*100)=10,000. -   4. The difference between the CSWS and the OQWS PMS is:     (12,500*10,000)=2,500,     -   and the difference with the IAI PMS is:         (14,900−10,000)=4,900.

In this example the IAI PMS difference factor is 0.3, smaller than unity, resulting in a final MLS score of: (2,500*1+4,900*0.3)=3,970.

Using the CSWS and the PMS results is a good estimation of similarity between the matched items. However, The reason that a simple SWS matching does not provide a good enough indication of similarity between an IQ and an IAI lies in the nature of NL queries, which dictates that any added NL word to a query may alter its meaning, and thus should yield different results.

For example: Assuming a SI containing 2 IAIs: “Definition of Algorithm”, as IAI1 and “What is the best known algorithm for image processing”, as IAI2. Matching both IAIs with the IQ “What is an algorithm”, a person can easily deduce that the better match is IAI1: “Definition of Algorithm”. Using only the SWS for comparison, will generate a higher score for IAI2 because it contains all of the NL words from the Query, while IAI1 includes only 1 matching word, and thus using only the SWSs will result in a bad match.

However, a quick look at the difference between the PMS and the CSWS will show that due to the excess of words in IAI2 with respect to the number of words in IQ1, the PMS will be much higher and thus result in a higher difference, while at the same time, the PMS for the IQ will be lower and much closer to the PMS of IAI1 and will result in a very small difference—prioritizing it as the better match.

Using difference based scoring allows detecting the difference of meaning between long queries to short items in the index SI and vice-versa, effectively and quantitatively describing the difference between general and specific queries, and general and specific Knowledge-base entries (IAIs in the present disclosure). The apriori removal of irrelevant portions of a query, as described above, may help to prevent distortions this process may cause when a query includes additional NLWs unrelated to the query. For example, the query “how in god's name can I get a pizza delivered to this location?” may be converted to “how can I get a pizza delivered to this location?”, prior to processing, such that the appearance of the words “in god's name” in the query and not in the IAI will not reduce the match level score.

Typically, PMS scores for IAIs can be stored in the index to avoid re-calculation during each comparison.

According to further embodiments, a MLS may be influenced by similarities and dissimilarities in contexts of the relevant IQ and IAI, such that similarities in contexts may result in an increase in the MLS (a bonus) and dissimilarities in contexts may result in a decrease in the MLS (a penalty). According to further embodiments, different types of contexts may result in different bonuses/penalties. For example, similarity or dissimilarity in a location context (e.g. US) may be more significant (and therefore applied more weight) than similarity or dissimilarity in a gender context (e.g. male).

Limiting Index Traversal

As the MLS for each traversed IAI is calculated during the traversal, and considering that traversal of the index generally starts from highest SL scoring NLW in the OQWS and either highest or best matching SL in the respective IKW LOR (depending on the used search strategy), it can be assumed that traversal follows the order of probability for finding good matches, meaning that the longer traversal continues, the less probable it is to find a better matching IAI than was already traversed. For that reason, in another aspect of the invention, a limitation of traversal is applied, that aims to stop the traversal of IKW LOR when the perceived resulting MLS degradation rises beyond a certain threshold to shorten the search process and decrease resource consumption. The traversal limitation is achieved typically by using the best MLS calculated in the course of a current traversal or current search operation (that may include the traversal of a plurality of LORs), and monitoring for degradation beyond a specific level, for example, when the calculated MLS for a traversed IAI is at least 2 times higher from the lowest (and best) MLS score observed during current LOR traversal.

Plurality of Reference Lists for a Single IAI

In the example of selecting a best matching indexed answer item for an IQ described above, each IAI had one single list of references associated. Each successive reference in that list has a decreased or equal SL with respect to the preceding one. In yet another aspect of the invention, a IAI may be associated with a plurality of reference lists. To explain the consequences of such a possibility reference is made to FIG. 4. IAIa designated 292, is associated with several different reference lists, such as reference lists 294 and 296 respectively. Thus, for a specific reference list, different PMS and SWS scores may be obtained, indicating different best matches for different IQs.

The reason for the existence of multiple reference lists is associated with several aspects of linguistic diversity, for example, usage of similar but different words to indicate a specific entity, spelling mistakes, or different variants of a same language. Comparison between quality of total scores associated with different reference list can be used to correct spelling mistakes, or unify spelling in the pre-processing procedure mentioned above.

Context Based Filtering

According to embodiments, context of an IQ and/or indexed answers items (IAIs) may be factored when performing the analysis and calculations described herein. According to some embodiments, some IAIs may be associated with a specific context (e.g. a specific geographical location such as US). In such embodiments, the process of matching IQs to IAIs may include factoring a similarity or dissimilarity in contexts of the IAIs in relation to the examined IQ, such that similarity in context between an IQ to an IAI may result in a bonus to the match score whereas a dissimilarity may result in a penalty/reduction in the match score. According to further embodiments, dissimilarity in context, in some case, may result in immediate disqualification of the IAI as a response to the IQ. For example, if an IQ is associated with the context “US”, all IAIs having a geographical context other than US (e.g. Europe) may immediately be disqualified.

According to some embodiments, a context of an IQ or IAI may include:

-   -   a. a user's activity on a website and/or a topic/context of one         or more pages of a website the user has visited, including, but         not limited to, pages visited, products viewed, products bought,         services bought or investigated, options selected, login         information, interactions on the site, and so on;     -   b. data relating to the user, including, but not limited to,         geographic location (country, city, etc.) possibly determined         based on IP analysis or other, IP address, user type (paying,         free, VIP, premium, etc.), personal information and demographics         (e.g.: gender, age, ethnicity, income, etc.), and so on;     -   c. data relating to the device the user is using, including, but         not limited to, device type (mobile phone, tablet, desktop),         device properties (screen size, hardware capabilities, installed         software, device settings), device manufacturer and price, and         so on;     -   d. Data relating to the current web page the IQ was entered in,         such as, but not limited to, HTML content, textual content,         script content, URL arguments, images, hidden content, forms,         and so on; and     -   e. Contextual data derived from the language of the IQ, such as         the choice of vocabulary and grammatical nuances.

According to some embodiments, determining a context of an IQ and/or IAI for calculation of match scores and/or SL scores, may include an analysis of the above mentioned parameters, which may include comparing data from multiple sources (e.g. based on IP address and user input), weighting different types of data, factoring the nature of the IQ, use of context models, and/or any other known form of contextual analysis. According to further embodiments, such an analysis may further include extraction of data from external sources based on collected contextual data (e.g. extracting ethnicity based on a surname from a name database, extracting economic status based on IP address from an appropriate database, etc.).

According to some embodiments, an IQ and/or IAI may be associated with multiple contexts (e.g. geo context, age context and gender context). In such embodiments, the system may factor more than one or all of the contexts when determining SL scores and match scores. Further, in embodiments where dissimilarity in context results in disqualification of IAIs, a dissimilarity in one of the contexts may be sufficient to disqualify an IAI. Yet further, different types of contexts may be treated differently, such that dissimilarity in one may result in disqualification whereas dissimilarity in another may only result in a penalty. For example, in a case where an IQ has contexts of US and female, an IAI with contexts of Europe and female may be disqualified whereas an IAI with a context of US and male may only incur a penalty.

According to some embodiments, IAIs may have multiple versions for different contexts (e.g. different version for Europe and the US or different versions for users sending their IQ from different websites, etc.). In such embodiments, there may also be a generic IAI for IQs having contexts not matching any of the context specific versions.

According to some embodiments, contexts of IQs may be divided into “Mandatory” contexts and “Optional” contexts. In such embodiments, a “Mandatory” context may indicate that IAIs lacking the context of the IQ (with or without a conflicting context)—are not returned as results (such that only users from the specified context will see the IAI). An “Optional” context may indicate that the system will favor results from a matching context, but will not enforce it, such that searches lacking the specified context (eg. users from a location other than a location specific context of an IAI) may still be exposed to the information.

According to some embodiments, contexts of IQs may be divided into “Explicit” contexts and “Implicit” contexts. In such embodiments, an “Implicit” context may be derived from an IQ and/or IAI based on the text of the IQ or IAI, whereas an “Explicit” context is derived by the system from a computational surrounding/environment of a user sending a query, irrespective of the text of the query (e.g. IP address, current website being used, user profile information, etc).

According to some embodiments, ‘Mandatory’ & ‘Explicit’ contexts may be used in user-type contexts (e.g. free/paying), security-level contexts, and other critical contexts. In other words, ‘Mandatory’ & ‘Explicit’ contexts may be used when a proprietor of the system wants to deny users not having the relevant contexts access to the specific IAIs, e.g. if the user hasn't paid for them. Another example of a case when an explicit context may be mandatory may be a commercial offer available only to certain users—e.g. an offer valid only in a specific location. In this case, a user from another location will not be presented with the answer including this offer even if his query specifically recites the relevant context (e.g. a user from Finland inquiring “how much does a_cost in Kansas?”—in this case this user may not be presented with an IAI describing a special offer available in Kansas, although the context (geo location) is clearly implicit to the query, as this IAI may have a mandatory explicit context of Kansas).

According to some embodiments, ‘Optional’ and ‘Implicit’ contexts may be used in less critical contexts, such as product-page, gender, etc. where a user is likely to hint on the relevant context in the search query, and there is no strict limitation on what information may or may not be displayed to a specific user.

Adding New IAIs to the Index and Removing Others

The process of adding IAIs into the index is a relatively straight-forward process, having understood the scoring and matching process of the invention. The basis for adding IAIs into the index is the creation of the IAI's ILOR.

To add a new IAI to the index, first an ILOR must be created with IKW REFs that is to be used to access the IAI. Typically, the ILOR is created by converting an IQ's OQWS to an IKW REF, by converting each NLW in the IQ's OQWS to an IKW REF with the same SL score that was calculated for the NLW in the OQWS. Each of the IKW REFs in the ILOR is then inserted into the appropriate IKW LOR in the index, at its appropriate sorted position. The addition of a new IAI item mandates an update of the SL score of the affected NLWs in both the private storehouse 26 and the language storehouse 30, as the occurrence frequency of the NLWs changes (additional occurrences have been presented). This change in SL score changes the potential SL scores and SWS and MLS scores of the entire search operation and requires a maintenance process to ensure consistency throughout the SI. Of course, context may also be considered when adding a new IAI.

Removing IAIs from the SI is the simple process of removing the IKW REFs from their respective LOR. This change also affects the frequency of the referenced NLWs and maintenance is required.

Maintenance of the SI and Language Storehouses

As the occurrence frequency of NLWs changes (due to addition of new IAIs, removal of IAIs etc.), the change normally affects the SL score of IKW REFs in the SI and possibly other indexes as well, as the NLW SL score of each IKW REF changes in accordance with the NLW SL score in both the private storehouse 26 and the language storehouse 30. Therefore, a subsequent re-calculation and re-positioning of every affected IKW REF should be applied. In addition, the SL score of NLWs and IKWs themselves is changed.

In another aspect of the invention, re-calculation and re-factoring of the various index entities (i.e NLW, 1KW REF etc.), which will be referred to hereinafter as the maintenance process, is managed in a way to only perform maintenance on IKW LORs that their associated NLW SL score has been modified beyond a specified factor, effectively limiting the amount of maintenance performed on the index considerably while still performing maintenance on items that are affected the most. Concurrently, the language and private storehouses of words are recompiled as a part of the maintenance process.

The present invention can be practiced by employing conventional tools, methodology and components. Accordingly, the details of such tools, component and methodology are not set forth herein in detail. In the previous descriptions, numerous specific details are set forth, in order to provide a thorough understanding of the present invention. However, it should be recognized that the present invention might be practiced without resorting to the details specifically set forth.

In the description and claims of embodiments of the present invention, each of the words, “comprise” “include” and “have”, and forms thereof, are not necessarily limited to members in a list with which the words may be associated.

Only exemplary embodiments of the present invention and but a few examples of its versatility are shown and described in the present disclosure. It is to be understood that the present invention is capable of use in various other combinations and environments and is capable of changes or modifications within the scope of the inventive concept as expressed herein.

While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention. 

The invention claimed is:
 1. A system for automatically matching a stored response to a user Natural Language Query (NLQ), said system comprising: a data storage containing indexed stared responses to natural language queries, each indexed stored response being associated with an individual set of natural language words and indexed based on the individual set of natural language words; a computing platform including processing circuitry adapted to: receive digital data representing the user NLQ; assign weight values to words in the user NLQ and to words in the individual sets of natural language words, based on a rate of occurrence of each of the words in at least one knowledgebase and rates of occurences of synonyms of each of the words in the at least one knowledgebase, such that a weight value assigned to a given word is inversely related to a rate of occurrence of the given word in the at least one knowledgebase and is further inversely related to rates of occurrences of synonyms of the given word in the at least one knowledgebase; identify responses stored in said data storage having an associated set of natural language words including words appearing in the user NLQ; identify responses stored in said data storage having an associated set of natural language words including synonyms of words appearing in the user NLQ; calculate a query specific significance value for words in the NLQ and words in each of the identified responses, wherein the query specific significance value for a given word in the NLQ is a ratio between the weight value of the given word in the NLQ and a sum of weight values of a set of other words in the NLQ and the query specific significance value for a given word in a given identified response is a ratio between the weight value of the given word in the given identified response and a sum of weight values of a set of other words in the given identified response, such that a query specific significance value of any selected word in any selected NLQ or identified response, is inversely related to a sum of weight values of words, other than the selected word, in the selected NLQ or identified response; and score matches between the user NLQ and each of the identified responses by performing, for each match of a given identified response and the user NLQ, a mathematical operation factoring: (i) query significance values of words in the user NLQ also appearing in the individual set of natural language words associated with the given identified response in said data storage; (ii) query significance values of words in the user NLQ synonymous to words appearing in the individual set of natural language words associated with the given identified response in said data storage, and (iii) query significance values of words in the identified responses corresponding to words in the NLQ.
 2. The system according to claim 1, wherein said processing circuitry is further adapted to compare a context in which the user NLQ was submitted to contexts associated with the identified responses, wherein a context in which the user NLQ was submitted is defined as parameters relating to the NLQ other than a text of the NLQ.
 3. The system according to claim 1, wherein said processing circuitry is further adapted to factor degrees of synonimity when assigning weight values to words in the user NLQ, and to words in the individual sets of natural language words, based on rates of occurences of synonyms of each of the words in the at least one knowledgebase.
 4. The system according to claim 3, wherein a degree of synonimity is context dependent.
 5. The system according to claim 1, wherein said processing circuitry is further adapted to factor degrees of synonimity when factoring weight values of words in the user NLQ synonymous to words appearing in an individual set of natural language words associated with a given identified response in said data storage.
 6. The system according to claim 5, wherein a degree of synonimity is context dependent.
 7. A system for automatically matching a stored response to a user Natural Language Query (NLQ), said system comprising: a data storage containing indexed stored responses to natural language queries, each indexed stored response being associated with an individual set of natural language words and indexed based on the individual set of natural language words; a computing platform including processing circuitry adapted to: receive digital data representing the user NLQ; assign weight values to words in the user NLQ and to words in the individual sets of natural language words, based on a rate of occurrence of each of the words in at least one knowledgebase, such that a weight value assigned to a given word is inversely related to a rate of occurrence of the given word in the at least one knowledgebase; identify responses stored in said data storage having an associated set of natural language words including words appearing in the user NLQ; identify responses stored in said data storage having an associated set of natural language words including synonyms of words appearing in the user NLQ; and calculate a query specific significance value for words in the NLQ and words in each of the identified responses, wherein the query specific significance value for a given word in the NLQ is a ratio between the weight value of the given word in the NLQ and a sum of weight values of a set of other words in the NLQ and the query specific significance value for a given word in a given identified response is a ratio between the weight value of the given word in the given identified response and a sum of weight values of a set of other words in the given identified response, such that a query specific significance value of any selected word in any selected NLQ or identified response, is inversely related to a sum of weight values of words, other than the selected word, in the selected NLQ or identified response: score matches between the user NLQ and each of the identified responses by performing, for each match of a given identified response and the user NLQ, a mathematical operation factoring: (i) query significance values of words in the user NLQ also appearing in the individual set of natural language words associated with the given identified response in said data storage; (ii) query significance values of words in the user NLQ synonymous to words appearing in the individual set of natural language words associated with the given identified response in said data storage, and (iii) query significance values of words in the identified responses corresponding to words in the NLQ.
 8. The system according to claim 7, wherein said processing circuitry is further adapted to compare a context in which the user NLQ was submitted to contexts associated with the identified responses, wherein a context in which the user NLQ was submitted is defined as parameters relating to the NLQ other than a text of the NLQ.
 9. The system according to claim 7, wherein said processing circuitry is further adapted to factor degrees of synonimity when factoring weight values of words in the user NLQ synonymous to words appearing in an individual set of natural language words associated with a given identified response in said data storage.
 10. The system according to claim 9, wherein a degree of synonimity is context dependent.
 11. A system for automatically matching a stored response to a user Natural Language Query (NLQ), said system comprising: a data storage containing indexed stored responses to natural language queries, each indexed stored response being associated with an individual set of natural language words and indexed based on the individual set of natural language words; a computing platform including processing circuitry adapted to: receive digital data representing the user NLQ; assign weight values to words in the user NLQ and to words in the individual sets of natural language words, based on a rate of occurrence of each of the words in at least one knowledgebase and rates of occurences of synonyms of each of the words in the at least one knowledgebase, such that a weight value assigned to a given word is inversely related to a rate of occurrence of the given word in the at least one knowledgebase and is further inversely related to rates of occurrences of synonyms of the given word in the at least one knowledgebase; identify responses stored in said data storage having an associated set of natural language words including words appearing in the user NLQ; calculate a query specific significance value for words in the NLQ and words in each of the identified responses, wherein the query specific significance value for a given word in the NLQ is a ratio between the weight value of the given word in the NLQ and a sum of weight values of a set of other words in the NLQ and the query specific significance value for a given word in a given identified response is a ratio between the weight value of the given word in the given identified response and a sum of weight values of a set of other words in the given identified response, such that a query specific significance value of any selected word in any selected NLQ or identified response, is inversely related to a sum of weight values of words, other than the selected word, in the selected NLQ or identified response; and score matches between the user NLQ and each of the identified responses by performing, for each match of a given identified response and the user NLQ, a mathematical operation factoring: (i) query significance values of words in the user NLQ also appearing in the individual set of natural language words associated with the given identified response in said data storage, and (ii) query significance values of words in the identified responses corresponding to words in the NLQ.
 12. The system according to claim 11, wherein said processing circuitry is further adapted to compare a context in which the user NLQ was submitted to contexts associated with the identified responses, wherein a context in which the user NLQ was submitted is defined as parameters relating to the NLQ other than a text of the NLQ.
 13. The system according to claim 11, wherein said processing circuitry is further adapted to factor degrees of synonimity when assigning weight values to words in the user NLQ, and to words in the individual sets of natural language words based rates of occurences of synonyms of each of the words in the at least one knowledgebase.
 14. The system according to claim 13, wherein a degree of synonimity is context dependent. 